System and method for classification of particles in a fluid sample

ABSTRACT

A particle classifier system and a method of training the system are described. The particle classifier system is suitable for classification of particles in a liquid sample, said particle classifier system comprises
         an optical detection assembly comprising at least one image acquisition device with an optical axis, the image acquisition device is configured to acquire images of an image acquisition area perpendicular to said optical axis;   a sample device comprising at least one sample container suitable for holding a sample in liquid form;   a translating arrangement configured to translate said image acquisition area through at least a part of said sample container;   a control system configured to controlling said optical detection assembly and said translating unit to acquire images of a plurality of image acquisition areas;   an image analyzing processing system programmed to analyze said acquired images by a method comprising
 
creating objects (sub-images) of individual particles captured by said acquired images,
 
creating stacks of objects of each individual particle,
 
identifying complete stacks of objects comprising
 
at least one object wherein said particle is in-focus, and
 
two objects wherein said particle is out-of-focus, and
 
determining, for each of said complete stacks of objects, a set of values for a set of features of at least N features, wherein N is larger than or equal to 1, and wherein the determination of said values of said set of features involve data obtained from said at least one object wherein said particle is in-focus, and/or said at least two objects wherein said particle is out-of-focus; and
   an artificial intelligent processing system programmed to associate said set of values for said determined set of features for each individual particle to a particle classification.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a continuation of application Ser. No. 14/654,347,filed on Jun. 19, 2015, which is the U.S. National Phase applicationunder 35 U.S.C. § 371 of International Application No.PCT/DK2013/050445, filed on Dec. 19, 2013, which claims the benefit ofU.S. Provisional Patent Application No. 61/739,323, filed on Dec. 19,2012 and Denmark Patent Application No. PA 2012 70800, filed on Dec. 19,2012. These applications are hereby incorporated by reference herein.

TECHNICAL FIELD

The present invention relates to a particle classifier system suitablefor classification of particles in a liquid sample, a method fortraining a particle classifier system comprising an artificialintelligent processing system, a method of analyzing a test liquidsample for the presence of a target particle and a method ofclassification of particles in a liquid sample. The particle classifiersystem and the methods may be utilized in automatic classification ofparticles in fluids, such as water, blood, urine, milk and similarfluids.

PRIOR ART

In U.S. Pat. No. 249,082 from 2006 Holmertz et al. discloses a methodand apparatus for counting somatic cells and fat droplets in milk. Theapparatus acquires images of raw milk flowing past the camera in a tube.The images comprise particles of fat droplets, somatic cells and otherparts of the milk. The particles are imaged in focus with a high spatialresolution, and after acquisition of the images, they are processeddigitally—preferably by using a neural network. The output from theapparatus is a fat droplet count or somatic cell count, from which theconcentration is calculated using estimated values for the size of themeasuring chamber. The apparatus requires a large number of images(thousands) to get a sufficiently high accuracy of the concentration.

In U.S. Pat. No. 5,715,182 from 1998 Asai et al. discloses a device forclassification and examination of particles in fluid. In the presenteddevice a fluid such as urine flows down in a flow cell past a camera,wherein the camera acquires images of the urine in focus. Only oneparticle is imaged at a time, and after acquisition, a unit extractscharacteristics of the particle imaged in focus. Then thecharacteristics are fed to a neural network trained to differentiatebetween the particles in question.

In the mentioned prior art classification devices, the particles in thefluid must pass the camera one at a time. This is very time consuming asmany images must be acquired for a reasonably good statistic of theresulting classification. Further, only one image (in focus) is acquiredof each particle, but as several different particles may look quiteequal, it may be difficult to classify them precisely.

U.S. Pat. No. 7,469,056 from 2008 Ramm et al. discloses a system forperforming automated cell screening in drug discovery, including anautomated microscope, a fast autofocus device and a digital imagingsystem. Processes are implemented in software through which relevantcellular material is segmented and quantified with minimal userinteraction.

WO 2009/091318 by Lindberg et al. discloses a method and apparatus foranalysis of particles in a liquid sample. The apparatus disclosed issuitable for acquiring a number of images from a liquid samplecomprising particles. The method disclosed comprises identifying imagesof particles in best focus and using the image in best focus for theparticle for identifying the type of particle.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a particleclassifier system suitably for classification of particles in a liquidsample and a method of classification of particles in a liquid samplewhich is simple and reliable, and where the particle classifier systemcan be trained in a fast and simple way.

It is further an object of the invention to provide a method fortraining a particle classifier system comprising an artificialintelligent processing system which method is relatively fast andsimple.

It is further an object to provide a method of analyzing a test liquidsample for the presence of a target particle, which method is reliableand fast.

These and other objects have been solved by the invention as defined inthe claims and as described herein below.

It has been found that the invention and embodiments thereof have anumber of additional advantages which will be clear to the skilledperson from the following description.

The particle classifier system suitably for classification of particlesin a liquid sample according to the invention comprises

-   -   an optical detection assembly comprising at least one image        acquisition device with an optical axis, the image acquisition        device is configured to acquire images of an image acquisition        area perpendicular to the optical axis;    -   a sample device comprising at least one sample container        suitable for holding a sample in liquid form;    -   a translating arrangement configured to translate the image        acquisition area through at least a part of the sample        container;    -   a control system configured to controlling the optical detection        assembly and the translating unit to acquire images of a        plurality of image acquisition areas;    -   an image analyzing processing system programmed to analyze the        acquired images by a method comprising        -   creating objects (sub-images) of individual particles            captured by the acquired images,        -   creating stacks of objects of each individual particle,        -   identifying complete stacks of objects comprising            -   at least one object wherein the particle is in-focus,                and            -   two object wherein the particle is out-of-focus, and        -   determining, for each of the complete stacks of objects, a            set of values for a set of features of at least N features,            wherein N is at least 1, and wherein the determination of            the values of the set of features involve data obtained from            the at least one object wherein the particle is in-focus,            and/or the at least two objects wherein the particle is            out-of-focus; and    -   an artificial intelligent processing system programmed to        associate the set of values for the determined set of features        for each individual particle to a particle classification.

The terms ‘liquid sample’ and ‘fluid sample’ are used interchangeable.

A ‘sub-image’ is a segment of an acquired image whereas the term‘object’ is used to denote a sub-image which has been selected forfurther use. Advantageously an object is a sub-image comprising an imageof one single particle, preferably the object has passed a pre-test thatit comprises an image of one single particle.

In an embodiment, for improved result a sub-image is approved to be anobject when it comprises an image of one single particle.

In an embodiment one or more objects are provided by objects witherror(s) in form sub-images having errors such as where a part of theobject is blurred, where the particle is not fully shown and/or whereseveral particles are clumping. Such one or more objects with errors arepreferably only used where a sufficient number of approved objects ofone single particle is/are not available.

A ‘stack’ of objects is a collection of objects of the same particle. Astack usually comprises one object in-focus and a number of objectsout-of-focus.

In an embodiment the stack of object comprises a plurality of approvedobjects of one single particle. Advantageously all objects of the stackis in form of approved objects of one single particle. However in anembodiment the stack of object comprises a plurality of approved objectsof one single particle and one or more objects with errors.

A ‘feature’ is a property for a stack, such as a size, a circumference,a color, etc. A feature value is the determined e.g. calculated valuefor a given feature, and is advantageously in form of a number (such as17, 9, or 0.23). In an embodiment the value of a feature is in form of acode—e.g. a color code. All features are to be determined from imagesacquired of a particle and advantageously determined from objects.Feature extraction and/or feature value determination is advantageouslyobtained by data-mining.

In an embodiment a number of feature types, such as size or shape arepreselected. The optical detection assembly, the sample device, thetranslating arrangement and the control system may individually or incombination be as they are known from prior art, e.g. as described in WO2010/063293, US 2011/0261164, EP 2031 428 and US 2009/0295963. Theseelements are well known in the art. The requirements of these componentsin combination are that they are capable of holding a liquid sample andare configured to acquire images of a plurality of image acquisitionareas within a fixated liquid sample. Preferred examples are describedbelow.

In an embodiment the image acquisition device is a scanning cameraconfigured to scan through at least a part of a liquid in the samplecontainer, the image acquisition device preferably being configured toscan through at least a part of a liquid in the sample container in ascanning direction different from the optical axis.

The sample device advantageously comprises a plurality of identical ordifferent sample containers.

The translating arrangement is advantageously configured to translatethe image acquisition area with a step length between each acquiredimage. The step length is preferably a preselected step length. The steplength is utilized when determining the distance between objects in thestacks of objects.

In an embodiment the translating arrangement is configured to move thesample device and the optical detection assembly relative to each other,thereby translating the image acquisition area through at least a partof the sample container.

The image analyzing processing system is programmed to analyze theacquired images. The image analyzing processing system (also referred toas the analyzing device) advantageously comprises a memory onto whichthe acquired images are stored. Advantageously also data regarding theposition of the acquired images are stored such that data regarding theposition of objects and optionally other sub-images can be retrieved tobe stored with the objects e.g. together with the position of thesub-image in the original image (i.e. the acquired image). The size andother relevant data may for example be stored as Meta data in thesub-image.

The objects of individual particles captured by the acquired images areobtained by identifying an image area which appears to comprise only oneparticle. The segmentation advantageously comprises a process ofpartitioning a digital image into multiple segments (sets of pixels,also known as super pixels). The goal of segmentation is to simplifyand/or change the representation of an image into something that is moremeaningful and easier to analyze. Image segmentation is typically usedto locate objects and boundaries (lines, curves, etc.) in images. In anembodiment the image segmentation comprising the process of assigning alabel to every pixel in an image such that pixels with the same labelshare certain visual characteristics.

Advantageously the acquired image is first scanned for bad regions suchas regions with a poor light level, regions where an item outside thesample container may have obscured the image, regions with signs of flowduring the image acquisition, etc. These regions are then discarded fromthe rest of the procedure. Subsequently a segmentation of the particlesin the rest of the acquired image is performed.

The segmentation advantageously comprises identification of each segmentin the image that may appear to be an image of a particle. Preferablyeach identified segment is copied from the rest of the image and thissub-image is advantageously applied to a number of filters, such as ashape-filter, a size-filter, a contrast-filter, intensity filter, etc.

When a sub-image is accepted to comprise an image of a particle (in orout of focus), it is called an “object”. When all possible objects inthe original image has been identified and logged, the original imagemay be stored for later use.

In an embodiment the sub-image is accepted to comprise an image of aparticle if the sub-image passes one or more filters and the sub-imageis then candidate to comprise an image of a particle, and the sub-imageis therefore logged and stored. When a sub-image is accepted, it ispromoted to be an object.

The creating of stacks of objects of each individual particle isprovided by using the data relating to the position of the objects inthe liquid sample relative to positions of other objects. Thereby it canbe determined which objects shows the same particles in or out of focus.

Each of the objects is added to one of a number of stacks where eachstack comprises objects of the same particle. Advantageously each stackcomprises information regarding the position where it was found in theliquid sample and/or the respective objects comprises informationregarding the position where it was found in the acquired image. Usingthe position information further objects may be connected to apreviously created stack comprising objects of the same particle. If theobject cannot be related to a previously created stack, a new stack isadvantageously created.

In an embodiment blind segmentation is utilized. In blind segmentation,a new stack is created when an object is found wherein a particle isimaged in focus. Further sub-images are added to the stack bycalculating the position in previous and subsequent acquired images andcut-out sub-images of similar size at these positions. In this way thereis no guarantee that the sub-images comprises an image of a particle,but the probability is high and for some particle types—in particularrelatively large particles—and/or for some particle classifiersystems—in particular where the distance between acquired images isrelatively low—this procedure works well. Such sub-images found by blindsegmentation are referred to a “blind objects”. The number of blindobjects in a stack may be e.g. 3 or more, 5 or more or even 7 or more.The blind objects are considered to be objects and are treated as suchin the image analyzing processing.

A stack may comprise as many objects as desired. Where the translatingarrangement comprises a step motor or similar step-wise translatingarrangement, the number of objects of a stack will depend on thestep-size. Naturally also the size of the particle is relevant for thenumber of objects in an object stack. Furthermore the depth-of-focus isrelevant. In an embodiment each stack comprises from 3 to 100 objects.Advantageously each stack comprises from 5 to 50 objects.

In an embodiment, the image analyzing processing system is programmed toanalyze the acquired images by a method comprising creating sub-images(objects) of individual particles captured by the acquired images,wherein each object is created by copying all pixels within a sub-imageperiphery line surrounding the individual particle and subjecting thecopied pixels to a filtration in at least one filter preferablycomprising a shape-filter, a size-filter, a contrast-filter or anintensity filter.

The object periphery line surrounding the individual particle may inprinciple have any shape. In an embodiment the object periphery linesurrounding the individual particle is preferably rectangular quadratic,circular, or shaped as a polygon.

In an embodiment, the analyzing processing system is programmed toanalyze the acquired images by a method comprising creating objects(sub-images) of individual particles captured by the acquired images andproviding stacks of the objects of the individual particles, wherein theobjects comprises only one particle each.

In an embodiment, the image analyzing processing system is programmed toanalyze the acquired images by a method comprising creating objects ofindividual particles captured by the acquired images and providingstacks of the objects of the individual particles, wherein the objectscomprises a particle free area surrounding the particle. Preferably theparticle free area is detectable by the image analyzing processingsystem.

In an embodiment, the image analyzing processing system is programmed toidentify complete stacks of objects, wherein the complete stacks ofobjects comprises at least one out-of focus object acquired on eitherside on the in-focus object. This provides an increased reliability ofthe system.

In an embodiment, the image analyzing processing system is programmed toidentify complete stacks of objects, wherein the complete stacks ofobjects comprises at least 5 objects, such as at least 9 objects, suchas at least 15 objects, such as at least 25 objects, such as at least 49objects,

In an embodiment, the image analyzing processing system is programmed todetermine values for a predetermined set of features of at least Nfeatures for each of the complete stacks of objects, wherein N is 3 ormore, such as 4 or more, such as up to about 1000.

Some particles are transparent for optical microscopy when imaged infocus, but may be partly opaque when imaged out of focus. Theseparticles will therefore be detected as objects in the out of focuspositions, but will leave a “hole” in the stack when in focus. An“artificial” object from the image may advantageously be applied for anin-focus object.

When no further objects are added to a stack, the stack is to be closedand it is evaluated if the stack is complete in that it is determined ifthe stack comprises at least one object of the particle in-focus, and atleast two images of the particle out-of-focus.

For each object in a stack a focus figure of merit or focus value isadvantageously calculated. The focus value is a quantification of howclose an object is to be imaged in focus. The higher the focus value,the closer the object is to be in best focus. The focus values for astack may be plotted. In an embodiment the focus values for a stack isused in the classification in that feature values from stacks with ahigher focus value are weighted higher than feature values from stackswith lower focus value.

Advantageously, there is at least one object on each side of the objectof the particle in focus. If the stack is complete the stack isconsidered to be accepted and get the status of being processed.Otherwise the stack of objects is discarded.

When a stack has been processed, it is ready for feature extraction. Alarge number of features may be defined and calculated and for each ofthe complete stacks of objects, a set of values for a set of features ofat least N features are determined, wherein N is at least 1, and whereinthe determination of the values of the set of features involve dataobtained from the at least one object wherein the particle is in-focus,and/or the at least two objects wherein the particle is out-of-focus.

The obtained set of values is processed in the artificial intelligentprocessing system programmed to associate the set of values for thedetermined set of features for each individual particle to a particleclassification.

The artificial intelligent processing system is in an embodimentintegrated with the image analyzing processing system and optionallyshare memory.

In an embodiment the artificial intelligent processing system and theimage analyzing processing system are separate elements arranged to bein digital connection e.g. wireless and/or via the Internet.

In an embodiment the particle classifier system comprises two operatingmodes comprising a training mode and a classification mode. In order tousing the particle classifier system for classifying, the particleclassifier system should be trained as described in further detailbelow. Due to the stacking of the objects and the generation of the setsof values for the stack, it has been found that the particle classifiersystem is relatively simple and fast to train. By a few trainingprocesses the particle classifier system will be capable of classifyingselected particle types. Further the particle classifier system can betrained using a very simple method which may be performed by users afterhaving received few instructions.

In an embodiment the particle classifier system in its training mode isprogrammed to associate the values of the set of features with aclassification provided by a user of the particle classifier system.

Each set of features associated with a classification is advantageouslystored in a memory.

In an embodiment the particle classifier system in its classificationmode is programmed to classify a particle based on a set of featuresfrom objects of the particle and using the artificial intelligentprocessing system comprising a plurality of sets of features associatedwith respective classification stored on the memory.

The artificial intelligent processing system may be any artificialintelligent processing system suitably for classifying the particles.The artificial intelligent processing system advantageously isprogrammed to have a classification algorithm. In an embodiment theartificial intelligent processing system is programmed to apply astatistic balancing algorithm. Thereby the artificial intelligentprocessing system is capable of classifying a particle using both exactmatches with a learned value set as well as statistical variationsthereof.

Advantageously the artificial intelligent processing system comprises aneural network, such as a feed forward artificial neural networkcomprising a plurality of layers of nodes. According to the inventionthe image analyzing processing system is programmed to for each of acomplete stack of objects, determining a set of values for a set offeatures of at least N features. The image analyzing processing systemof the particle classifier system advantageously is programmed todetermine sets of values for a large number of features, which—dependenton the type of particles to be classified—can be selected or deselected.

The features may be any features which alone or in combination withother features can be used to distinguish one type of particles fromanother type of particles.

Many different features may be defined and implemented. Each of the manyfeatures may be determined e.g. calculated for every object stack, butusually a sub-set of features is selected. The features in the sub-setshould be selected to provide as much information regarding thedifference between the different kinds of particles as possible.

One feature may be the circumference of the object. This feature shouldadvantageously be selected if the particles have different sizes as itwill provide useful information regarding the type of particle, but ifthe particles are of approximately the same size this feature should notbe selected or be applied alone

When characterizing particles comprised in a fluid such as urine, whereone would look for e.g. bacteria, crystals and white blood-cells, oneset of features may be optimal, but the same set of features may notprovide sufficiently information when the fluid is milk. In milk adifferent subset may be used for characterizing particles such as fatdroplets and somatic cells (in case of mastitis).

In an embodiment the classifier system is suitably for classification ofparticles of biological or non-biological origin or a combinationthereof. The biological particles may be selected from the group ofbacteria, archaea, yeast, fungi, pollen, viruses, blood cells, such asleukocytes, erythrocytes, and thrombocytes; oocytes, sperm, zygote, stemcells, somatic cells, crystals, fat droplets and mixtures thereof.

The features may comprise features based on a thresholded object infocus, such as:

-   -   spatial descriptors such as area, length of perimeter, area of        enclosing circle etc. and/or    -   morphological descriptors such as convexity, eccentricity, shape        factor etc. and/or    -   binary moments

The features may also comprise features based on a grayscale version ofan object in focus, such as

-   -   contrast, light scattering properties, absorption etc. and/or    -   various types of grayscale moments and/or    -   features extracted in the Fourier space of the focused grayscale        image, and/or    -   Granularity

The features may also comprise features based on a color version of anobject in Focus, for example

-   -   pre-dominant color pattern and/or    -   hue.

Further the features may comprise features based on information from theobject stack (i.e. a number of objects in and out of focus), such as

-   -   signatures/descriptors of various focus curves of the object,        such as FWHM, AUC, variance between the curve and a smoothed        curve etc. and/or    -   signatures/descriptors of various intensity curves of the        object, such as FWHM, AUC, variance between the curve and a        smoothed curve etc. and/or    -   signatures/descriptors of curves generated by applying        grayscale/binary features to individual objects in the object        stack,    -   assessment of temporal parameters of the stack,    -   phase and absorption map, Brownian movement and self-propelled        characteristic, and/or    -   flow characteristic.

In an embodiment the image analyzing processing system is programmed todetermining values for a set of features comprising at least one of

-   -   features relating to out-of-focus objects,    -   features relating to grayscale versions of in-focus objects,    -   features relating to color versions of in-focus objects,        features relating to thresholded versions of in-focus objects        and/or    -   features relating to both in-focus and out-of-focus objects

In an embodiment the features relating to out-of-focus objects maycomprise one of

-   -   circumference of the particle (shape),    -   size of particle (cross-sectional area),    -   ratio between the largest and the smallest diameter,    -   color variation (degree of color variation) and/or    -   pre-dominant color pattern.

In an embodiment the features relating to in-focus objects comprises atleast one of

-   -   circumference of the particle (shape),    -   size of particle (cross-sectional area),    -   ratio between the largest and the smallest diameter,    -   color variation (degree of color variation),    -   predominant color pattern, and/or    -   number of sub-particles inside the circumference of the        particle.

In an embodiment the features relating to both out-of-focus objects andin-focus objects comprises at least one of

-   -   difference(s) in circumference of the particle (shape) from one        object to another,    -   difference(s) in size of particle (cross-sectional area) from        one object to another,    -   difference(s) in ratio between the largest and the smallest        diameter from one object to another,    -   difference(s) in color variation (degree of color variation)        from one object to another,    -   difference(s) in predominant color pattern from one object to        another,    -   difference(s) in color from one object to another and/or    -   distance between respective object.

When a selected set of features has been determined for an object stack,the feature values are passed to the next step in the procedure—thefinal classification. In an embodiment a feature set is advantageously alist of values such as (17, 3, 199, . . . , 0.11), where each value is adetermined e.g. calculated value for a specific feature. In anembodiment the feature set is in form of a binary pattern.

In an embodiment the selection of features is simplified by usingdifferent kinds of scatter plots in two or more dimensions.

In an embodiment the selection of features comprises data mining to findsignificant features

-   -   such as features that differ significantly from one particle        type to another.

Advantageously the artificial intelligent processing system is capableof associating a set of features with a specific class of particles, theset of features is obtained from a complete stack of objects of aparticle and the associating of the a set of features with the specificclass of particles is based on a statistic balancing and processing of aplurality of sets of features associated with the class of particles anda plurality of sets of features associated with at least one other classof particles.

In an embodiment the values for each feature in a set of features arecalculated for each stack, but the particles remain unclassified. Thenthe sets of features are combined into an ensemble of features. Thisensemble covers all particles in the fluid sample, and to this ensemblea set of features may be assigned and determined. An example of anensemble feature may be the average size of the particles, but could beany feature determined for the stacks. Further, the features for theensemble comprise features related to the shape or density of a plot ofthe features. The set of features for the ensemble may be utilized asinformation for classifying each individual particle relative to theensemble. The result is a set of particle features, which are definedrelative to the ensemble.

In an embodiment the particle classifier system is adapted tosequentially acquire sets of images and classify particles captured by afirst set of images and particles captured by at least a second set ofimages, creating a first ensemble of stacks of objects from the firstset of images and a second ensemble of stacks of objects from the secondset of images.

The number of sets of images may be as high as desired. Advantageouslythe respective sets of images are obtained by respectively scanning'sthrough the sample or a part of the sample where a time delay isprovided between the respective scanning's. Thereby changes of therespective particles can be observed. This embodiment is very suitablyfor analyzing of biological particles e.g. living particles. In that waygrowth can be analyzed. This method may also be applied for analyzingdecay of particles. The time delay between the respective scanning's canbe selected in dependence on the expected rate of the changes which arelikely to occur.

An ensample of stacks comprises at least one stack and advantageously aplurality of stacks. In an embodiment the system is adapted to identifystacks of objects of the first ensemble of individual particles andstacks of objects of the second ensemble of the respective individualparticles and detect changes between the stacks of objects of the firstensemble and stacks of objects of the second ensemble of the respectiveindividual particles. The detection of changes can advantageously beperformed immediately after a scanning and the time delay to asubsequent scanning can be regulated to provide a desired degree ofchanges. Thereby the development time of one or more particles can bedetermined.

In an embodiment the system is adapted to determine Brownian movement ofa particle from objects in a stack.

Advantageously a set of features of at least N features is determinedfor stacks of objects of the ensembles where the N features can be asdescribed above.

In an embodiment the particles are reclassified using features from theensembles of stacks of objects. For example the stacks of objects of thefirst ensemble of individual particles may result in a firstclassification and the stacks of objects of a second or a furtherensemble of individual particles can result in a reclassification e.g. afiner classification because further data is obtained.

In an embodiment the image analyzing processing system is adapted todetermine values for a set of features for each stack of objects ofparticles that have not yet been classified (unclassified particles),and to create an ensemble of sets of features, and wherein the imageanalyzing processing system is adapted to classify these previouslyunclassified particles using the ensemble of sets of features.

When training a particle classifier system there are several proceduresthat may be followed. One approach is to feed the particle classifiersystem with a liquid sample comprising only one type of particle. Theparticles are then classified to the same class, and a set of featuresmay be selected that best describes the particle.

This may be done several times using different types of particles in thesample, and for several samples comprising the same type of particles.In this way a library of stacks may be created where every type ofparticle is represented by many stacks of objects.

The optimum set of features for one type of particle may not be theoptimum set for another type of particle. When two or more differenttypes of particles are present in one liquid sample, a combined set offeatures may be selected among all the sets of features that give thebest possible classification of the particles.

The invention therefore also relates to a method for training a particleclassifier system. The method comprises using an artificial intelligentprocessing system for classifying of at least one individual particle ina liquid sample, the method comprises

-   -   providing one or more liquid samples comprising together at        least one first type of particle and at least one second type of        particle,    -   acquiring at least 3 images of a plurality of the first type of        particle and a plurality of the second type of particle in the        liquid sample, wherein the individual first type and second type        of particles respectively are in-focus or out-of-focus,    -   creating objects (sub-images) of the individual particles        captured by the acquired images and providing stacks of objects        for each first type of particle and for each second type of        particle and identifying complete stacks of objects comprising        at least one object wherein the particle is in-focus, and two        objects wherein the particle is out-of-focus,    -   determining a set of features of at least N features for each of        stacks of objects, wherein N is larger than 0, and wherein the        determination of the features involve data obtained from the at        least one object wherein the particle is in-focus, and the at        least two objects wherein the particle is out-of-focus, and    -   training the artificial intelligent processing system by feeding        it with data comprising first type sets of data associated with        a first type of a particle class and second type sets of data        associated with a second type of a particle class.

The method for training may utilize a particle classifier system similarto the system described earlier for classifying particles.

In an embodiment, the method comprises providing a liquid samplecomprising a plurality of a first type of particles and a plurality of asecond type of particles and training the artificial intelligentprocessing system using the sets of data obtained from the particles inthe provided liquid samples. This may be done for several liquidssamples preferably with different concentrations of the first type andthe second type of particles.

A simpler approach is to use a training method where only one type ofparticle is present in the liquid sample. This is preferably done usingseveral different liquid samples of the same particles, i.e. the methodcomprises providing at least one liquid sample comprising exclusivelyone type of particle and training the artificial intelligent processingsystem using sets of data obtained from the particles in the providedliquid sample(s).

In some cases it may be very difficult or impossible to obtain a liquidsample comprising only one type of sample. In these situations it issufficient to know that a certain percentage of the particles are of onetype only.

The rest of the particles may be of several different types. Duringtraining the classifier system is advantageously provided with theinformation that a percentage e.g. 75% of the particles are of one type,and the rest of the particles are of one or more different types. Theclassifier system may then be adjusted so as the output equals thiswanted result.

In an embodiment the method for training comprises providing at leastone liquid sample comprising a predominantly amount of one type ofparticles, preferably at least about 75% of the particles, such as atleast about 80% of the particles, such as at least about 85% of theparticles, such as at least about 90% of the particles, such as at leastabout 95% of the particles are the same type of particle; and trainingthe artificial intelligent processing system using sets of data obtainedfrom the particles in the provided liquid sample(s).

In an embodiment the method comprises providing at least one liquidsample comprising only a first type of particles and providing at leastone liquid sample comprising only a second type of particles andtraining the artificial intelligent processing system using sets of dataobtained from the particles in the provided liquid samples.

In an embodiment the method for training the classifier system comprisesusing supervised learning or a semi-supervised learning.

In supervised learning a skilled person classifies a number of particlesand feed the results to the classifier system. When a preferable largenumber of particles has been manually classified and feed to theclassifier system, a set of features may be selected so as to let theclassifier system give the same results as obtained manually. Whenutilizing a semi-supervised learning system, a skilled person classifiesa number of particles and feed the results to the classifier system. Theclassifying system will continuously learn and get better in making acorrect classification of new particles. For new particles theclassifying system proposes a classification and the skilled personaccepts or rejects the proposed classification. In case of a rejection,the skilled person may correct the classification and thereby providingmore “knowledge” or experience to the classifying system.

In an embodiment the method for training a classifier system comprisesproviding one or more liquid samples comprising together T differenttypes of particles where T is 3 or more, providing complete stacks forthe particles, determining features from the stacks and training theartificial intelligent processing system using the sets of featuresassociated with the respective type of particle class.

In some situations a screening of a fluid sample for specific types ofparticles is needed. In these cases the fluid sample may be consideredto comprise particles of a target type and particles of no specificinterest.

In an embodiment the method for training a classifier system a firsttype particles are of a target type and a second type particles are notof the target type.

In an embodiment of the method for training a classifier system thetarget type is particles selected from the group of bacteria, archaea,yeast, fungi, pollen, viruses, leukocytes, such as granulocytes,monocytes, Erythrocytes, Thrombocytes, oocytes, sperm, zygote, stemcells, somatic cells, crystals, fat droplets and mixtures thereof.

When a system has been trained with fluid samples comprising a set oftarget particles, the system may be utilized for screening a test fluidfor the presence of one or more of the target particles.

The artificial intelligent processing system is advantageouslyperforming the processing using a classification algorithm. The input tothe classification algorithm comprises the values for the sub-set offeatures, and the output is the type of particle (or a code for suchparticle), such as bacteria, fat droplet, white blood cell, red bloodcell, crystal, etc. etc. In an embodiment a counter has been assigned toeach of the expected types of particles, and the counter for theparticular identified type of particle is increased by one each time aparticle has been identified.

Where the artificial intelligent processing system performing theprocessing using a classification algorithm it can for example betrained as described above.

In an embodiment when the classification algorithm has been applied onall processed stacks and the particle type determined, a volume of theliquid may be determined. The volume may be determined using informationfrom the number of full images acquired, the step size of thetranslating arrangement and the discarded parts of the images.

When the volume has been determined, an overall particle concentrationmay easily be calculated, as well as particle concentrations of eachtype of particle. Finally it may be decided if the overall number ofparticles and the different particle concentrations are sufficient for areliable measurement result, or if acquisition of a new image should beinitiated and added to the result. When the total result of themeasurement is determined to be sufficiently accurate, the measurementprocedure is stopped.

In an embodiment of the method of analyzing a liquid sample the methodcomprises sequentially acquire sets of images and classify particlescaptured by a first set of images and particles captured by at least asecond set of images, creating a first ensemble of stacks of objectsfrom the first set of images and a second ensemble of stacks of objectsfrom the second set of images preferably as described in the abovedescription of the system.

In an embodiment a characterization of the whole sample or a fractionthereof is obtained by determine sets of features of an ensemble ofstacks of objects and determine an ensample feature array also referredto as a feature cloud, which can for example provide information atcloud level that cannot be assessed direct from features at particlelevel, but which can be detected from a plurality of stacks of objectsfrom an ensample.

By this method sets of features of stacks can be used as data points ina feature space, where each point represents a particle, and thereby thefeature cloud can be determined.

Examples of characterizations that can be determined by cloud feature(s)are cloud moments, cloud shape parameter, number of clusters, clouddensity and etc.

In an embodiment the cloud feature(s) can provide information abouttemperature, pressure, entropy, etc. due to the general appearance ofthe particles in the sample.

In an embodiment the method comprising correlating or comparingdetermined cloud feature(s) to determinations provided on known samplesand thereby assessing the state or characteristics of the sample.

Samples populated with identical specimens, e.g. bacteria or yeast, andgrown at equal conditions are expected to have same clouddensity/distribution in the feature space. This means any cloud featureis expected to be statistical equal.

Any changes of the sample that occur due to internal or external effectsare expected to change the cloud density/distribution, and thus alterthe corresponding cloud features which in turn can be used tocharacterize the state of the population in the sample.

Examples of effects that can be detected by use of cloud features e.g.by cloud correlation are

-   -   nutrition    -   environment such as temperature, pH, antibiotics    -   contamination (A sample is contaminated by a secondary        population that evolves to a detectable level over time)    -   breading (Concentration increases and a secondary effect may be        evolving, distribution of sizes)    -   activity    -   mutation (may change the shape of the cloud due to appearance of        new values of particle feature in the cloud)    -   structural mode/development (cluster formation, different life        cycle of specimens, ageing, live/dead)

The assessment of population state using cloud features e.g. cloudcorrelation may be divided into characterization of a sample or timeevolution of particles in a sample.

In an embodiment a method is utilized for analyzing a test liquid samplefor the presence of a target particle using a trained particleclassifier system, the particle classifier system being trained toclassify the target particle, the method comprising

-   -   providing the test liquid sample,    -   acquiring at least 3 images of individual particles in the        liquid sample, wherein the particles respectively are in-focus        or out-of-focus,    -   creating sub-images (objects) of the individual particles        captured by the acquired images and providing stacks of objects        for each individual particle and identifying complete stacks of        objects comprising at least one object wherein the particle is        in-focus, and two objects wherein the particle is out-of-focus,    -   determining a set of features of at least N features for each of        the stacks of objects, wherein N is at least 1, and    -   allowing the artificial intelligent processing system to        associate the set of values for the determined set of features        for each individual particle to classify the particles and        determine if at least one target particle has been classified.

In some situations it is desired to do more than just detecting if atarget particle is present in a fluid sample. It may be desirable toknow the number of target particles present in a given volume of thefluid sample—i.e. the concentration. It may also be desirable to knowthe relative concentration compared to other types of particles, becausethere are more than one kind of target particle. This is the case whenusing blood for measuring. In an embodiment when determining the socalled 3 parts diff or 5 parts diff, the number of each of the differentwhite blood cells (neutrophils, lymphocytes, monocytes, eosinophil's andbasophils) is determined, and their relative concentrations areadvantageously calculated.

In an embodiment of the method for analyzing a test liquid, the methodcomprises performing a quantitative analyze determining the number oftarget particles in the test liquid.

In an embodiment the method for classification of particles in a liquidsample using a trained particle classifier system comprises

-   -   acquiring at least 3 images of the individual particle in the        liquid sample, wherein the individual particle is in-focus or        out-of-focus    -   creating sub-images (objects) of the individual particle and        assigning each of the objects to a stack of objects comprising        different images of the individual particle, such that the stack        of objects comprises at least one object wherein the particle is        in-focus, and two objects wherein the particle is out-of-focus,    -   determining a set of features of at least N features for the        stack of objects, wherein N is larger than 0, and wherein the        determination of the set of feature involve data obtained from        the at least one object wherein the particle is in-focus, and        the at least two objects wherein the particle is out-of-focus,    -   feeding the set of features to an artificial intelligent        processing system of a trained particle classifier system and        for classifying the individual particle, and    -   allowing the artificial intelligent processing system to        associate the set of values for the determined set of features        for each individual particle to classify the particles.

All features of the inventions including ranges and preferred ranges canbe combined in various ways within the scope of the invention, unlessthere are specific reasons not to combine such features.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present invention will now be described, byway of example, with reference to the accompanying drawings. It shouldbe understood that the description of these preferred embodiment shouldnot be interpreted to limit the scope of the invention

FIG. 1 shows an example of an optical detection assembly and a sampleholder comprising a sample of fluid comprising particles,

FIG. 2 shows an example of two images of a fluid sample comprising alarge number of particles,

FIG. 3 shows a schematic diagram of the system according to theinvention,

FIG. 4 shows an example of a focus curve calculated for a stack ofobjects.

FIGS. 5a, 5b 6a and 6b illustrates objects of two stacks of objects andfocus curves for the stacks obtained in example 1.

The figures are schematic and may be simplified for clarity. Throughout,the same reference numerals are used for identical or correspondingparts.

PREFERRED EMBODIMENTS

The system of the present invention comprises an optical detectionassembly. The optical detection assembly comprises in a preferredembodiment at least one image acquisition device comprised of aCCD-camera or a CMOS camera. The optical detection assembly further iscomprised of lenses, prisms, irises, apertures and other common opticalcomponents used in microscopy. The optical detection assembly is adaptedto acquire images wherein individual biological organisms may beidentified. One embodiment of an optical detection assembly is describedin US provisional application U.S. 61/146,850, wherein an apparatus forobtaining a plurality of images of a sample arranged in relation to asample device is provided. The apparatus comprises at least a firstoptical detection assembly comprising at least a first image acquisitiondevice. The first optical detection assembly has an optical axis and anobject plane. The object plane comprises an image acquisition area fromwhich electromagnetic waves can be detected as an image by the firstimage acquisition device. The apparatus further comprises at least onetranslating arrangement arranged to move the sample device and the firstoptical detection assembly relative to each other, and a housingarranged to support the first optical detection assembly and thetranslating arrangement, wherein the first optical detection assemblyand the translating arrangement are arranged so that at least a part ofthe sample device is intersected by the image acquisition area. Themovement of the sample device and the first optical detection assemblyrelative to each other is along a scanning path, which defines an angletheta relative to the optical axis, wherein theta is larger than zero.

U.S. 61/146,850 also discloses a method for obtaining a plurality ofimages of a sample. This method comprises arranging the sample inrelation to a sample device and arranging the sample device in relationto an apparatus for obtaining a plurality of images. The apparatuscomprises at least a first optical detection assembly having at least afirst image acquisition device. The first optical detection assembly ishaving an optical axis and an object plane, where the object plane hasan image acquisition area from which electromagnetic waves can bedetected as an image by the first image acquisition device.

The image acquisition area intersects at least a part of the sample. Thesample device and the first detection assembly are moved relative toeach other over a scanning length along a first scanning path. Thescanning path and the optical axis together define an angle theta, whichis larger than zero. The method furthermore comprises obtaining theplurality of images.

In U.S. 61/146,850, is further disclosed a system for obtaining aplurality of images of a sample. The system comprises a sample deviceand an apparatus having at least a first optical detection assemblycomprising at least a first image acquisition device. The first opticaldetection assembly of the apparatus has an optical axis and an objectplane. This object plane comprises an image acquisition area from whichelectromagnetic waves can be detected as an image by the first imageacquisition device. The apparatus of this system further comprises atleast one translating arrangement arranged to move the sample device andthe first optical detection assembly relative to each other, and ahousing arranged to support the first optical detection assembly and thetranslating arrangement, wherein the first optical detection assemblyand the translating arrangement are arranged so that at least a part ofthe sample device is intersected by the image acquisition area. Themovement of the sample device and the first optical detection assemblyrelative to each other is along a scanning path, which defines an angletheta relative to the optical axis, wherein theta is larger than zero.In principle, the scanning path of U.S. 61/146,850, may comprise anymovement of the object plane and the sample relative to each other. Inparticular, the scanning path may comprise a substantially straightscanning line arranged along a scanning axis. The scanning path may alsobe defined by a substantially rotational movement, in which case thetais the angle between the optical axis and the local tangential of therotational movement. In an embodiment, the scanning path is confined toa plane, such as a straight line, a circular movement, a spiralmovement, or any other suitable path.

A preferred embodiment of an optical detection assembly according to thepresent invention is shown in FIG. 1a , a sample device 32 comprising afluid sample 4 comprising a pluralities of particles 2, 2′, 2″, 2′″, 2″″is shown. The sample device 32 has a longitudinal translation axis Z. Alight source 24 is provided at one side of the sample holder, and anobjective lens 26 and an image sensor 8 is provided on the other side ofthe sample holder. The light source 24 transmits light 30 through thefluid sample 4 towards the image sensor 8, wherein images of theparticles 2, 2′, 2″, 2′″, 2″″ in the fluid sample 4 is acquired whenthey are in the field of view of the image sensor. A translatingarrangement (not shown) may translate the sample device 32 relative tothe image sensor 8 in small steps, and for each step a new image isacquired.

In FIG. 1b , the optical detection assembly comprising the image sensor,the lens 26, and the light source 24 has been moved in 4 steps in thescanning direction A. The particles 2, 2′, 2″, 2′″, 2″″ has been imagedby the image sensor in the positions 28, 28′, 28″, 28′″, 28″″. Some ofthe particles 2, 2′, 2″, 2′″, 2″″ may have been imaged in all 5positions 28, 28′, 28″, 28′″, 28″″, while some for the particles 2, 2′,2″, 2′″, 2″″ may only have been imaged in one or two of the images 28,28′, 28″, 28′″, 28″″. Particles 2, 2′, 2″, 2′″, 2″″ positioned close tothe image positions 28, 28′, 28″, 28′″, 28″″ is imaged in focus whileparticles 2, 2′, 2″, 2′″, 2″″ positioned further away) but still insidethe field of view of the image sensor) will be imaged out of focus.

The depth of field of the system, in connection with the step size ofthe translating arrangement, may be arranged to make sure, that a givenparticle is imaged in-focus in one image, and imaged out-of-focus in atleast one image on each side of the image, wherein the image isin-focus. I.e. if a particle 2″ is imaged in-focus in image 28″, then itwill be imaged out-of-focus in image 28′ and 28″. It may also be imagedout-of-focus in images 28 and 28″″, but the image quality may be toopoor to be used. In another example, the particle 2″ is imaged in-focusin image 28′, 28″ and 28′″, while the images 28 and 28″″ areout-of-focus.

In FIG. 2a , an image 28 acquired from a fluid in a sample holder isshown, the fluid comprising a large number of particles. Two particles 2and 2′ has been marked. In FIG. 2b , an image 28′ is shown, the image28′ being the next in line when acquiring images. Also in FIG. 2b twoparticles 2 and 2′ has been marked. As may be seen, the particle 2 isimaged in-focus in image 28 and out-of-focus in image 28′, while theopposite is the case for the particle marked 2′.

In FIG. 3, a schematic of a system according to the present invention isdisplayed. The system comprises an optical detection assembly 40comprising an image acquisition device 41 and a lens 41 a. The opticaldetection system may be a standard optical microscope with build-incamera. In FIG. 3 an optical microscope with oblique viewing is shown.An illumination device 43 transmits light, such as laser light or lightfrom a LED through a sample device 42 towards the optical detectionassembly 40. The fluid comprising particles to be imaged is contained inthe sample device 42. The system comprises a translating arrangement 44.The sample device 42 may be translated along Z-axis relative to theoptical detection assembly 40 using the translating arrangement 44,thereby moving particles in the fluid past the image acquisition zone.The step length of the movement is controlled by the translatingarrangement 44 so as to make sure that a plurality and advantageouslyall particles are imaged at least 3 times—one time in-focus and at leasttwo times out-of-focus.

The optimal step length may be determined by the optical system, i.e.the depth of field and the requirements for optical resolution. If thesystem has a large depth of focus, the step length may be increased,while a low depth of focus may call for small steps. A smaller depth offocus may provide a larger viewing angle, enabling a larger number ofparticles to be scanned at the same time, but also a poorer opticalresolution of the particles imaged.

The system further comprises a control system 46, adapted to control themovement of the translating arrangement 44 relative to the opticaldetection assembly 40 as well as controlling the image acquisition fromthe camera. The method for example comprises translating the sampledevice 42 one step, acquiring an image from the camera, and transferringthe image to an image analyzing device 48 of the system. While the imageanalyzing device 48 is analyzing the image, the control system 46initiates the translation for a new image to be acquired.

When an image arrives at the analyzing device 48 it is stored in amemory. The image is first scanned for bad regions such as regions witha high/low light level, regions where an object outside the samplecontainer may have obscured the image, etc. These regions are thendiscarded from the rest of the procedure. Then a segmentation of theparticles in the rest of the image is performed. Segmentation comprisesidentification of each segment in the image that may appear to be animage of a particle. Each identified segment is copied from the rest ofthe image and this sub-image is applied to a number of filters, such asa shape-filter, a size-filter, a contrast-filter, intensity filter, etc.If the sub-image passes these filters, the sub-image is candidate tocomprise an image of a particle, and the sub-image is therefore loggedand stored. Data regarding the position of the sub-image in the originalimage, the size and other relevant data is stored as Meta data in thesub-image. Usually a sub-image is rectangular, but it may in principlebe of any shape, as long as the shape is described in the Meta data.

When a sub-image is accepted to comprise an image of a particle (in orout of focus), it is called an “object”. When all possible objects inthe original image has been identified and logged, the original imagemay be stored for later use.

Each of the objects is then added to one of a number of stacks ofobjects. An object stack comprises objects identified in previousimages. Each stack comprise information regarding the position where itwas found in the image, and using this information new objects may beconnected to a previously created object stack comprising objects of thesame particle. If the object cannot be related to a previously createdstack, a new stack of objects is created.

A stack of objects is open as long as new objects are added to thestack. A stack may comprise many objects depending on the step-size ofthe translating arrangement and the depth-of-focus. Usually a stackcomprises from 1 to 20-25, but it may comprise as many as 45-50 objectsor even more. If a new image acquisition does not end up in adding a newobject to a particular stack, the stack may be closed. This may happenwhen the translating arrangement 44 has moved the camera out of rangefor the particle.

For each object in a stack a focus figure of merit or focus value iscalculated. The focus value is a quantification of how close an objectis to be imaged in focus. The higher the focus value is, the closer theobject is in focus. The focus values for a stack of objects 60 may beplotted as shown in FIG. 4 showing focus values as a function of ID,which is the identified order of the objects of the stack. As may beseen, the curve has a top point indicating that the object closest tothe top point is the object imaged in focus. The other objects on thecurve are imaged more or less out of focus.

The system further comprises an artificial intelligent processing system50 for classifying of particles. The artificial intelligent processingsystem is trained as described above. Advantageously the artificialintelligent processing system 50 and the image analyzing processingsystem are integrated in a common unit.

As may be seen from FIG. 4 two of the focus values F from objects 61 aremissing—indicated by a dotted square where they should be expected tobe. Sometimes during acquisition of images a region of an image isdiscarded, and if the discarded region comprises objects belonging to anopen object stack, the object will be missing leaving a “hole” in thefocus curve. It may therefore be advantageous to leave a stack ofobjects open even if no new objects are added to the stack.

Some particles are transparent for optical microscopy when imaged infocus, but may be partly opaque when imaged out of focus. Theseparticles will therefore be detected as objects in the out of focuspositions, but will leave a “hole” in the stack when in focus. This“hole” may be 1, 2 or even 5 or more positions depending on the stepsize and the depth of focus.

To fill out the holes in the object stacks it may therefore beadvantageous to cut out “artificial” objects from the image and add to astack for a number of positions until the out of focus objects appearsor the object is not discarded due to a bad region. If no new realobjects are added to the stack after a number of image acquisitions, theartificial objects may be removed from the stack and the stack may beclosed.

When a stack of objects has been closed, the stack is passed on to thenext level. In this level it is determined if the stack comprises atleast one object of the particle in-focus, and at least two images ofthe particle that may be out-of-focus. If there is at least one objecton each side of the object of the particle in focus, the stack isconsidered to be accepted and get the status of being processed.Otherwise the stack of objects is discarded.

When a stack has been processed, it is ready for feature extraction. Alarge number of features may be defined and calculated and only a fewwill be mentioned here. A skilled person in the art will appreciate thatmany more different features may be defined and calculated.

The features may advantageously be as described above and preferablycomprising features based on a thresholded object in focus, such as:

-   -   Spatial descriptors such as area, length of perimeter, area of        enclosing circle etc.    -   Morphological descriptors such as convexity, eccentricity, shape        factor etc.    -   Binary moments

The features may also comprise features based on a grayscale version ofan object in focus, such as

-   -   Contrast, light scattering properties, absorption etc.    -   Various types of grayscale moments    -   Features extracted in the Fourier power spectrum of the focused        grayscale image

Further the features may comprise features based on information from theobject stack (i.e. a number of objects in and out of focus), such as

-   -   Signatures/descriptors of various focus curves of the object,        such as FWHM, AUC, variance between the curve and a smoothed        curve etc.    -   Signatures/descriptors of various intensity curves of the        object, such as FWHM, AUC, variance between the curve and a        smoothed curve etc.    -   Signatures/descriptors of curves generated by applying        grayscale/binary features to individual objects in the object        stack.

At present, app. 80 different features have been defined and newfeatures is defined and implemented in the system regularly. Each of themany features may be calculated for every object stack, but usually asub-set of features is selected. The features in the sub-set should beselected to provide as much information regarding the difference betweenthe different kinds of particles as possible.

One feature may be the circumference of the object. This feature shouldbe selected if the particles have different sizes as it will provideuseful information regarding the type of particle, but if the particlesare of approximately the same size this feature should not be selected.

When characterizing particles comprised in a fluid such as urine, whereone would look for e.g. bacteria, crystals and white blood-cells, oneset of features may be optimal, but the same set of features may notprovide sufficiently information when the fluid is milk. In milk adifferent subset may be used for characterizing particles such as fatdroplets and somatic cells (in case of mastitis).

When a selected set of features has been calculated for an object stack,the feature values are passed to the next step in the procedure—thefinal classification. The feature set may be a list of values such as(17, 3, 199, . . . , 11) where each value is a calculated value for aspecific feature.

The set of features are transferred to the artificial intelligentprocessing system 50 for classifying of particles.

Example 1 Example: Viability of Yeast Cells

In this example a method of classifying yeast cells using a particleclassifier system as described above is described. The yeast cells areclassified as either “dead” or “alive”.

A liquid sample comprising a plurality of yeast cells were analyzedusing the particle classifier described above and a plurality of stackof objects were obtained, each stack comprising at least one in-focusand two out-of focus objects.

In FIG. 5 the objects of a stack of a yeast cell is shown. In the upperleft corner the object is negatively defocused. The objects are alignedin an order of left to right, row after row.

Following the order of the objects it can be seen that the focus Fgradually increases and finally arrives at perfect focus (in-focusobject), denoted by an arrow and framed with a fat line. The followingobjects are gradually defocused (positively defocus). In this way thecomplete focus behavior of a single yeast cell can be viewed in onecomposite image as shown in FIG. 5a . FIG. 5b shows a focus curve forthe stack of FIG. 5 a.

FIG. 6a shows objects of another stack of objects aligned in an ordercorresponding to the object of the stack of FIG. 5a . FIG. 6b shows afocus curve for the stack of FIG. 6 a.

The particle classifier system is classifying the yeast cell viewed inFIG. 5a as a dead yeast cell and the yeast cell of FIG. 6a as a livingyeast cell.

If looking only at in-focus objects i.e. as indicated with the arrowthere is no distinct difference in the appearance and it is verydifficult—and sometimes even impossibly—to judge the viability (“dead”or “alive”) of a cell. No morphological or textural features will revealif the cell is dead or alive.

However, when using out-of-focus information of the yeast cell, it canbe seen that it becomes much simpler to judge the viability. For examplesome of the out-of-focus objects of FIG. 6a shows a bright spot in thecenter of the living yeast cell. The out-of focus objects of FIG. 5adoes not have that feature.

Further it can be seen that the focus curves as shown in FIGS. 5b and 6bdiffers significantly from each other. For the dead cells the focuscurve appears as unimodal Gaussian shaped curve, i.e. it only has asingle maximum. For the living cells the focus curve appears as abimodal Gaussian shaped curve, i.e. with two distinct maxima.

Example 2

Using Ensemble Features to Classify Particles:

In a sample of monoculture yeast cells of unknown size or shape, we wantto classify each cell according to the stage of spawning. It is assumedthat all cells are in a spawning state where they are either alone (notspawning), two cells connected (spawning with a single offspring), ormultiple objects connected (higher level of spawning). Aftersegmentation, stack generation and feature calculation, the sizedistribution of the particles will show peaks around multiples of asingle cell size, i.e. peaks around 1 unit size, 2 unit sizes, etc. Itis clear that the area corresponding to the lowest peak corresponds tothe size of a single cell, which was unknown up to this point. Allparticles in the ensemble can now be classified according to the peak inthe distribution they are closest to, i.e. particles closest to thelowest peak are not spawning, and particles closer to the second peakare classified as single offspring cells.

What is claimed is:
 1. A system for classifying particles in watersample, system comprising: an optical detection assembly comprising atleast one image acquisition device and corresponding lens with anoptical axis, the image acquisition device is configured to acquireimages of an image acquisition area perpendicular to the optical axis; asample device comprising at least one sample container suitable forholding a water sample in liquid form comprising particles; atranslating arrangement configured to translate said image acquisitionarea through at least a part of said sample container with an optimalstep length between each acquired image, wherein the optimal step lengthis determined based on a depth of field of the optical detectionassembly, and a selected optical resolution; a controller configured tocontrol said optical detection assembly and said translating arrangementto acquire images of a plurality of image acquisition areas; an imageprocessor programmed to analyze said acquired images and to determine aset of features for an individual particle captured by said acquiredimages by creating objects of the individual particle and assigning eachof the objects to a corresponding stack of objects comprising differentimages of the individual particle; and an artificial intelligentprocessor programmed to associate said set of features for saidindividual particle to a particle classification of particles, whereinsaid step length between each acquired image is used to determine adistance between the objects in the stacks of objects.
 2. The system asclaimed in claim 1 wherein said particle classifier system comprises twooperating modes comprising a training mode and a classification mode. 3.The system as claimed in claim 1, wherein: the image acquisition deviceacquires at least three images of the individual particle, wherein theindividual particle is in-focus or out-of-focus, the image processorcreates the objects of the individual particle captured by the at leastthree acquired images, such that the stack of objects comprises at leastone object wherein the individual particle is in-focus, and two objectswherein the individual particle is out-of-focus, and the image processorfurther determines a set of values for the set of features of at least Nfeatures, wherein N is larger than or equal to 1, and wherein thedetermination of the set of values of the set of features involves dataobtained from the at least one object wherein said particle is in-focus,and/or at least the two objects wherein the individual particle isout-of-focus.
 4. The system as claimed in claim 1, wherein saidtranslating arrangement is configured to move said sample device andsaid optical detection assembly relative to each other.
 5. A method ofcreating objects of individual particles in a water sample held in asample container, the method comprising: determining an optimal steplength based on a depth of field of the optical detection assembly, anda selected optical resolution; translating an image acquisition area,formed by an image acquisition device and corresponding lens in thesample container, to multiple positions in the sample container, theimage acquisition area being perpendicular to an optical axis of thelens; acquiring images of the image acquisition area at the multiplepositions in the sample container; determining a set of features for anindividual particle in the water sample captured by the acquired imagesby creating objects of the individual particle, wherein creating theobjects of the individual particle comprises: copying all pixels withinan object periphery line surrounding the individual particle; andsubjecting said copied pixels to a filtration in at least one filtercomprising a shape-filter, a size-filter, a contrast-filter or anintensity filter.
 6. The method of claim 5, wherein said objectperiphery line surrounding the individual particle is shaped as apolygon or a circle.
 7. The system as claimed in claim 1, wherein saidimage processor is programmed to identify complete stacks of objects,wherein said complete stacks of objects comprise at least one: (i) atleast 5 objects, (ii) at least 9 objects, (iii) at least 15 objects,(iv) at least 25 objects, and (v) at least 49 objects.
 8. The system asclaimed in claim 1, wherein said image processor is programmed todetermine values for a set of features comprising at least one offeatures relating to out-of-focus objects, features relating tograyscale versions of in-focus objects, features relating to thresholdedversions of in-focus objects, and features relating to both in-focus andout-of-focus objects.
 9. The system as claimed in claim 1, wherein theparticle classification of particles is selected from a group consistingof bacteria, archaea, yeast, fungi, pollen, viruses, leukocytes,granulocytes, monocytes, Erythrocytes, Thrombocytes, oocytes, sperm,zygote, stem cells, somatic cells, crystals, fat droplets and mixturesthereof.
 10. The system as claimed in claim 1, wherein the at least oneimage acquisition device is adapted to sequentially acquire sets ofimages and the artificial intelligent processor classifies particlescaptured by a first set of images and particles captured by at least asecond set of images, creating a first ensemble of stacks of objectsfrom said first set of images and a second ensemble of stacks of objectsfrom said second set of images.
 11. The system as claimed in claim 10,wherein said system is adapted to identify stacks of objects of saidfirst ensemble of individual particles and stacks of objects of saidsecond ensemble of said respective individual particles and detectchanges between said stacks of objects of said first ensemble and stacksof objects of said second ensemble of said respective individualparticles.
 12. The system as claimed in claim 1, wherein said imageprocessor is adapted to determine values for a set of features forunclassified particles, and to create an ensemble of sets of features,and wherein said unclassified particles are classified using saidensemble of sets of features.
 13. A method of analyzing a test watersample held in a sample container for the presence of a target particle,the method comprising: providing the test water sample; determining anoptimal step length based on a depth of field of the optical detectionassembly, and a selected optical resolution; translating an imageacquisition area, formed by an image acquisition device andcorresponding lens in the sample container, to multiple positions in thesample container; acquiring at least 3 images of individual particles,respectively, in said test water sample at multiple positions in thesample container, wherein said individual particles respectively arein-focus or out-of-focus; creating objects of said individual particles,respectively, captured by said acquired images and providing stacks ofobjects for each individual particle; identifying complete stacks ofobjects, each identified complete stack of objects at least comprisingan object wherein the corresponding individual particle is in-focus andtwo objects wherein the corresponding individual particle isout-of-focus; determining a set of features of at least N features foreach of said identified complete stacks of objects, wherein N is largerthan or equal to 1; and associating said set of values for saiddetermined set of features for each individual particle to classify saidindividual particle and to determine if at least one target particle hasbeen classified from among the individual particles.
 14. The method asclaimed in claim 13 further comprising: performing a quantitativeanalysis for determining the number of target particles in said testwater sample.